Flow cytometry for mouse exp17: competition with sfgfp and mcherry

---

Authored: Kathy N. Lam

Date: January 13, 2020

Updated: April 12, 2021

Set up

library(flowCore) #for reading and manipulating flow data
library(ggcyto) #for using ggplot with flow data
library(scales) #for nice plot axes
library(cowplot) #for multi panel plots
library(Phenoflow) #for rarefying
library(tidyverse) #for data wrangling and plotting

Read data

(fs = read.flowSet(path="data_fcs"))

## A flowSet with 100 experiments.
## 
##   column names:
##   530/30 Blue B-A 610/20 YG C-A FSC-A FSC-H FSC-W SSC-A SSC-H SSC-W Time

colnames(fs) 

## [1] "530/30 Blue B-A" "610/20 YG C-A"   "FSC-A"           "FSC-H"          
## [5] "FSC-W"           "SSC-A"           "SSC-H"           "SSC-W"          
## [9] "Time"

pData(phenoData(fs))

#read in sample info
metadata = read_tsv("metadata.tsv") %>%
    rename(name=Filename) %>%
    mutate(Mouse = paste("Mouse", Mouse)) %>%
    mutate(Day = paste("Day", Timepoint)) %>%
    mutate(Treatment_Mouse = paste0(Treatment, "\n", Mouse)) %>%
    mutate(Mouse_Treatment = paste0(Mouse, " (", Treatment, ")"))

## Parsed with column specification:
## cols(
##   FlowSampleNumber = col_double(),
##   `Mouse Sample Number` = col_double(),
##   Experiment = col_character(),
##   Treatment = col_character(),
##   Mouse = col_double(),
##   Timepoint = col_double(),
##   Processing = col_character(),
##   CollectEvents = col_character(),
##   Filename = col_character(),
##   Notes = col_logical()
## )

metadata$Mouse = factor(metadata$Mouse, levels=unique(metadata$Mouse))
metadata$Treatment_Mouse = factor(metadata$Treatment_Mouse, levels=unique(metadata$Treatment_Mouse))
metadata$Treatment = factor(metadata$Treatment, levels=unique(metadata $Treatment))
metadata$Day = factor(metadata$Day, levels=unique(metadata$Day))
metadata

#add columns to phenoData  
phenoData(fs)$Order = seq(1, length(phenoData(fs)$name))
phenoData(fs)$Treatment = metadata$Treatment
phenoData(fs)$Timepoint = metadata$Timepoint
phenoData(fs)$Day = metadata$Day
phenoData(fs)$Name = metadata$name
phenoData(fs)$Mouse = metadata$Mouse
phenoData(fs)$Treatment_Mouse = metadata$Treatment_Mouse
phenoData(fs)$Mouse_Treatment = metadata$Mouse_Treatment
pData(phenoData(fs))

#make labeller function for facet_wrap
order = as.character(phenoData(fs)$Order)
name = phenoData(fs)$Name

order_names = mapply(c, order, name, SIMPLIFY = FALSE) #make a one-to-one 
order_names = lapply(order_names, `[[`, 2) #keep second element of each vector in the list
order_names = order_names[as.character(sort(as.numeric(names(order_names))))] #numerically sort 

order_labeller = function(variable,value){
  return(order_names[value])
}

Gate on scatter

scatter = rbind(c(0,   1e4), 
                c(1e5, 1e4),
                c(1e5, 2.5e5),  
                c(0,   2.5e5))
colnames(scatter)=c("FSC-A", "SSC-A")
scatter = as.data.frame(scatter)

ggplot() + 
    geom_point(data=fs, aes(x=`FSC-A`, y=`SSC-A`), shape=16, size=0.75, alpha=0.5) +
    scale_y_continuous(name="SSC-A (Granularity)\n", limits = c(-2e1,3e5)) +
    scale_x_continuous(name="\nFSC-A (Size)", limit=c(-2e1,3e5)) +
    facet_grid(Mouse~Timepoint) +
    theme_linedraw(14) +
    theme(panel.grid = element_blank(), axis.text.x=element_text(angle=90,hjust=1)) +
    geom_polygon(data=scatter, aes(x=`FSC-A`, y=`SSC-A`), fill=NA, colour="indianred", size=0.5,  linetype="solid") 

## Warning: Removed 29191 rows containing missing values (geom_point).

gate_scatter = polygonGate(filterId="scatter", `FSC-A` = scatter$`FSC-A`, `SSC-A` = scatter$`SSC-A`) 
result = flowCore::filter(fs, gate_scatter)
events = flowCore::Subset(fs, result)

Rarefy

events = Phenoflow::FCS_resample(events, replace = FALSE)

## Your samples range between 57249 and 90877 cells
## Your samples were randomly subsampled to 57249 cells

Gate on fluoresence

red = rbind(
    c(-1e3,   4e3),
    c(-1e3,   4e5),
    c( 4000,  4e5),
    c( 1000,  4e3))
colnames(red)=c("530/30 Blue B-A", "610/20 YG C-A")
red = as.data.frame(red)

green = rbind(
    c(9e2,   -1e3),
    c(4.5e3,  800),
    c(1.5e5,    800),
    c(1.5e5,   -1e3))
colnames(green)=c("530/30 Blue B-A", "610/20 YG C-A")
green = as.data.frame(green)

ggplot() + 
    geom_polygon(data=green, aes(x=`530/30 Blue B-A`, y=`610/20 YG C-A`), fill=NA, colour="green4", size=0.5, linetype="solid") +
    geom_polygon(data=red, aes(x=`530/30 Blue B-A`, y=`610/20 YG C-A`), fill=NA, colour="red3", size=0.5, linetype="solid") +
    geom_point(data=events, aes(x=`530/30 Blue B-A`, y=`610/20 YG C-A`), shape=16, size=0.5, alpha=0.2) +
    theme_linedraw(12) +
    ggcyto::scale_y_flowjo_biexp(name="mCherry intensity", widthBasis=-500, pos=5, neg=0, breaks=c(0, 1e3,1e4,1e5), limits=c(-1e3, 4e5)) +
    ggcyto::scale_x_flowjo_biexp(name="GFP intensity", widthBasis=-500, pos=5, neg=0, breaks=c(0, 1e3,1e4,1e5), limits=c(-1e3, 1.5e5)) +
    theme(panel.grid = element_blank(), 
          axis.title.x = element_text(colour="green4"), 
          axis.title.y = element_text(colour="red3")) +
    facet_grid(Mouse~Timepoint)  

## Warning: Removed 50 rows containing missing values (geom_point).

gate_green = polygonGate(filterId="green", `530/30 Blue B-A` = green$`530/30 Blue B-A`, `610/20 YG C-A` = green$`610/20 YG C-A`)
result_green = flowCore::filter(events, gate_green)
events_green = Subset(events, result_green)

gate_red = polygonGate(filterId="red", `530/30 Blue B-A` = red$`530/30 Blue B-A`, `610/20 YG C-A` = red$`610/20 YG C-A`)
result_red = flowCore::filter(events, gate_red)
events_red = Subset(events, result_red)

percent_red = toTable(summary(result_red)) %>% 
    mutate(x=2e5, y=2e5, colour="red", order=phenoData(events)$order) %>%
    rename(name=sample) %>%
    left_join(metadata, by="name")

## filter summary for frame 'Specimen_001_Sample_001_001.fcs'
##  red+: 5 of 57249 events (0.01%)
## 
## filter summary for frame 'Specimen_001_Sample_002_002.fcs'
##  red+: 2120 of 57249 events (3.70%)
## 
## filter summary for frame 'Specimen_001_Sample_003_003.fcs'
##  red+: 3441 of 57249 events (6.01%)
## 
## filter summary for frame 'Specimen_001_Sample_004_004.fcs'
##  red+: 859 of 57249 events (1.50%)
## 
## filter summary for frame 'Specimen_001_Sample_005_005.fcs'
##  red+: 87 of 57249 events (0.15%)
## 
## filter summary for frame 'Specimen_001_Sample_006_006.fcs'
##  red+: 2 of 57249 events (0.00%)
## 
## filter summary for frame 'Specimen_001_Sample_007_007.fcs'
##  red+: 6104 of 57249 events (10.66%)
## 
## filter summary for frame 'Specimen_001_Sample_008_008.fcs'
##  red+: 2302 of 57249 events (4.02%)
## 
## filter summary for frame 'Specimen_001_Sample_009_009.fcs'
##  red+: 24637 of 57249 events (43.03%)
## 
## filter summary for frame 'Specimen_001_Sample_010_010.fcs'
##  red+: 15171 of 57249 events (26.50%)
## 
## filter summary for frame 'Specimen_001_Sample_011_011.fcs'
##  red+: 11 of 57249 events (0.02%)
## 
## filter summary for frame 'Specimen_001_Sample_012_012.fcs'
##  red+: 3714 of 57249 events (6.49%)
## 
## filter summary for frame 'Specimen_001_Sample_013_013.fcs'
##  red+: 1308 of 57249 events (2.28%)
## 
## filter summary for frame 'Specimen_001_Sample_014_014.fcs'
##  red+: 474 of 57249 events (0.83%)
## 
## filter summary for frame 'Specimen_001_Sample_015_015.fcs'
##  red+: 67 of 57249 events (0.12%)
## 
## filter summary for frame 'Specimen_001_Sample_016_016.fcs'
##  red+: 0 of 57249 events (0.00%)
## 
## filter summary for frame 'Specimen_001_Sample_017_017.fcs'
##  red+: 3736 of 57249 events (6.53%)
## 
## filter summary for frame 'Specimen_001_Sample_018_018.fcs'
##  red+: 4 of 57249 events (0.01%)
## 
## filter summary for frame 'Specimen_001_Sample_019_019.fcs'
##  red+: 4 of 57249 events (0.01%)
## 
## filter summary for frame 'Specimen_001_Sample_020_020.fcs'
##  red+: 0 of 57249 events (0.00%)
## 
## filter summary for frame 'Specimen_001_Sample_021_021.fcs'
##  red+: 4 of 57249 events (0.01%)
## 
## filter summary for frame 'Specimen_001_Sample_022_022.fcs'
##  red+: 7861 of 57249 events (13.73%)
## 
## filter summary for frame 'Specimen_001_Sample_023_023.fcs'
##  red+: 360 of 57249 events (0.63%)
## 
## filter summary for frame 'Specimen_001_Sample_024_024.fcs'
##  red+: 40 of 57249 events (0.07%)
## 
## filter summary for frame 'Specimen_001_Sample_025_025.fcs'
##  red+: 4 of 57249 events (0.01%)
## 
## filter summary for frame 'Specimen_001_Sample_026_026.fcs'
##  red+: 30 of 57249 events (0.05%)
## 
## filter summary for frame 'Specimen_001_Sample_027_027.fcs'
##  red+: 3829 of 57249 events (6.69%)
## 
## filter summary for frame 'Specimen_001_Sample_028_028.fcs'
##  red+: 65 of 57249 events (0.11%)
## 
## filter summary for frame 'Specimen_001_Sample_029_029.fcs'
##  red+: 8 of 57249 events (0.01%)
## 
## filter summary for frame 'Specimen_001_Sample_030_030.fcs'
##  red+: 3 of 57249 events (0.01%)
## 
## filter summary for frame 'Specimen_001_Sample_031_031.fcs'
##  red+: 1 of 57249 events (0.00%)
## 
## filter summary for frame 'Specimen_001_Sample_032_032.fcs'
##  red+: 5329 of 57249 events (9.31%)
## 
## filter summary for frame 'Specimen_001_Sample_033_033.fcs'
##  red+: 10 of 57249 events (0.02%)
## 
## filter summary for frame 'Specimen_001_Sample_034_034.fcs'
##  red+: 5 of 57249 events (0.01%)
## 
## filter summary for frame 'Specimen_001_Sample_035_035.fcs'
##  red+: 26 of 57249 events (0.05%)
## 
## filter summary for frame 'Specimen_001_Sample_036_036.fcs'
##  red+: 0 of 57249 events (0.00%)
## 
## filter summary for frame 'Specimen_001_Sample_037_037.fcs'
##  red+: 10327 of 57249 events (18.04%)
## 
## filter summary for frame 'Specimen_001_Sample_038_038.fcs'
##  red+: 33 of 57249 events (0.06%)
## 
## filter summary for frame 'Specimen_001_Sample_039_039.fcs'
##  red+: 8 of 57249 events (0.01%)
## 
## filter summary for frame 'Specimen_001_Sample_040_040.fcs'
##  red+: 315 of 57249 events (0.55%)
## 
## filter summary for frame 'Specimen_001_Sample_041_041.fcs'
##  red+: 3 of 57249 events (0.01%)
## 
## filter summary for frame 'Specimen_001_Sample_042_042.fcs'
##  red+: 3916 of 57249 events (6.84%)
## 
## filter summary for frame 'Specimen_001_Sample_043_043.fcs'
##  red+: 20 of 57249 events (0.03%)
## 
## filter summary for frame 'Specimen_001_Sample_044_044.fcs'
##  red+: 47 of 57249 events (0.08%)
## 
## filter summary for frame 'Specimen_001_Sample_045_045.fcs'
##  red+: 2194 of 57249 events (3.83%)
## 
## filter summary for frame 'Specimen_001_Sample_046_046.fcs'
##  red+: 0 of 57249 events (0.00%)
## 
## filter summary for frame 'Specimen_001_Sample_047_047.fcs'
##  red+: 3476 of 57249 events (6.07%)
## 
## filter summary for frame 'Specimen_001_Sample_048_048.fcs'
##  red+: 3000 of 57249 events (5.24%)
## 
## filter summary for frame 'Specimen_001_Sample_049_049.fcs'
##  red+: 12405 of 57249 events (21.67%)
## 
## filter summary for frame 'Specimen_001_Sample_050_050.fcs'
##  red+: 1567 of 57249 events (2.74%)
## 
## filter summary for frame 'Specimen_001_Sample_051_051.fcs'
##  red+: 0 of 57249 events (0.00%)
## 
## filter summary for frame 'Specimen_001_Sample_052_052.fcs'
##  red+: 2140 of 57249 events (3.74%)
## 
## filter summary for frame 'Specimen_001_Sample_053_053.fcs'
##  red+: 9827 of 57249 events (17.17%)
## 
## filter summary for frame 'Specimen_001_Sample_054_054.fcs'
##  red+: 7463 of 57249 events (13.04%)
## 
## filter summary for frame 'Specimen_001_Sample_055_055.fcs'
##  red+: 6184 of 57249 events (10.80%)
## 
## filter summary for frame 'Specimen_001_Sample_056_056.fcs'
##  red+: 5 of 57249 events (0.01%)
## 
## filter summary for frame 'Specimen_001_Sample_057_057.fcs'
##  red+: 5156 of 57249 events (9.01%)
## 
## filter summary for frame 'Specimen_001_Sample_058_058.fcs'
##  red+: 13248 of 57249 events (23.14%)
## 
## filter summary for frame 'Specimen_001_Sample_059_059.fcs'
##  red+: 4429 of 57249 events (7.74%)
## 
## filter summary for frame 'Specimen_001_Sample_060_060.fcs'
##  red+: 15858 of 57249 events (27.70%)
## 
## filter summary for frame 'Specimen_001_Sample_061_061.fcs'
##  red+: 3 of 57249 events (0.01%)
## 
## filter summary for frame 'Specimen_001_Sample_062_062.fcs'
##  red+: 2627 of 57249 events (4.59%)
## 
## filter summary for frame 'Specimen_001_Sample_063_063.fcs'
##  red+: 8 of 57249 events (0.01%)
## 
## filter summary for frame 'Specimen_001_Sample_064_064.fcs'
##  red+: 1 of 57249 events (0.00%)
## 
## filter summary for frame 'Specimen_001_Sample_065_065.fcs'
##  red+: 19357 of 57249 events (33.81%)
## 
## filter summary for frame 'Specimen_001_Sample_066_066.fcs'
##  red+: 1 of 57249 events (0.00%)
## 
## filter summary for frame 'Specimen_001_Sample_067_067.fcs'
##  red+: 5302 of 57249 events (9.26%)
## 
## filter summary for frame 'Specimen_001_Sample_068_068.fcs'
##  red+: 8 of 57249 events (0.01%)
## 
## filter summary for frame 'Specimen_001_Sample_069_069.fcs'
##  red+: 4 of 57249 events (0.01%)
## 
## filter summary for frame 'Specimen_001_Sample_070_070.fcs'
##  red+: 13 of 57249 events (0.02%)
## 
## filter summary for frame 'Specimen_001_Sample_071_071.fcs'
##  red+: 3 of 57249 events (0.01%)
## 
## filter summary for frame 'Specimen_001_Sample_072_072.fcs'
##  red+: 3372 of 57249 events (5.89%)
## 
## filter summary for frame 'Specimen_001_Sample_073_073.fcs'
##  red+: 7220 of 57249 events (12.61%)
## 
## filter summary for frame 'Specimen_001_Sample_074_074.fcs'
##  red+: 5401 of 57249 events (9.43%)
## 
## filter summary for frame 'Specimen_001_Sample_075_075.fcs'
##  red+: 1174 of 57249 events (2.05%)
## 
## filter summary for frame 'Specimen_001_Sample_076_076.fcs'
##  red+: 20 of 57249 events (0.03%)
## 
## filter summary for frame 'Specimen_001_Sample_077_077.fcs'
##  red+: 2674 of 57249 events (4.67%)
## 
## filter summary for frame 'Specimen_001_Sample_078_078.fcs'
##  red+: 17890 of 57249 events (31.25%)
## 
## filter summary for frame 'Specimen_001_Sample_079_079.fcs'
##  red+: 15952 of 57249 events (27.86%)
## 
## filter summary for frame 'Specimen_001_Sample_080_080.fcs'
##  red+: 7398 of 57249 events (12.92%)
## 
## filter summary for frame 'Specimen_001_Sample_081_081.fcs'
##  red+: 0 of 57249 events (0.00%)
## 
## filter summary for frame 'Specimen_001_Sample_082_082.fcs'
##  red+: 2191 of 57249 events (3.83%)
## 
## filter summary for frame 'Specimen_001_Sample_083_083.fcs'
##  red+: 35866 of 57249 events (62.65%)
## 
## filter summary for frame 'Specimen_001_Sample_084_084.fcs'
##  red+: 16459 of 57249 events (28.75%)
## 
## filter summary for frame 'Specimen_001_Sample_085_085.fcs'
##  red+: 1252 of 57249 events (2.19%)
## 
## filter summary for frame 'Specimen_001_Sample_086_086.fcs'
##  red+: 2 of 57249 events (0.00%)
## 
## filter summary for frame 'Specimen_001_Sample_087_087.fcs'
##  red+: 4489 of 57249 events (7.84%)
## 
## filter summary for frame 'Specimen_001_Sample_088_088.fcs'
##  red+: 10552 of 57249 events (18.43%)
## 
## filter summary for frame 'Specimen_001_Sample_089_089.fcs'
##  red+: 14525 of 57249 events (25.37%)
## 
## filter summary for frame 'Specimen_001_Sample_090_090.fcs'
##  red+: 9585 of 57249 events (16.74%)
## 
## filter summary for frame 'Specimen_001_Sample_091_091.fcs'
##  red+: 1 of 57249 events (0.00%)
## 
## filter summary for frame 'Specimen_001_Sample_092_092.fcs'
##  red+: 2822 of 57249 events (4.93%)
## 
## filter summary for frame 'Specimen_001_Sample_093_093.fcs'
##  red+: 7235 of 57249 events (12.64%)
## 
## filter summary for frame 'Specimen_001_Sample_094_094.fcs'
##  red+: 11861 of 57249 events (20.72%)
## 
## filter summary for frame 'Specimen_001_Sample_095_095.fcs'
##  red+: 10834 of 57249 events (18.92%)
## 
## filter summary for frame 'Specimen_001_Sample_096_096.fcs'
##  red+: 2 of 57249 events (0.00%)
## 
## filter summary for frame 'Specimen_001_Sample_097_097.fcs'
##  red+: 2111 of 57249 events (3.69%)
## 
## filter summary for frame 'Specimen_001_Sample_098_098.fcs'
##  red+: 10397 of 57249 events (18.16%)
## 
## filter summary for frame 'Specimen_001_Sample_099_099.fcs'
##  red+: 24615 of 57249 events (43.00%)
## 
## filter summary for frame 'Specimen_001_Sample_100_100.fcs'
##  red+: 1622 of 57249 events (2.83%)

percent_green = toTable(summary(result_green)) %>%
    mutate(x=2e5, y=1e5, colour="green", order=phenoData(events)$order) %>%
    rename(name=sample) %>%
    left_join(metadata, by="name")

## filter summary for frame 'Specimen_001_Sample_001_001.fcs'
##  green+: 173 of 57249 events (0.30%)
## 
## filter summary for frame 'Specimen_001_Sample_002_002.fcs'
##  green+: 4451 of 57249 events (7.77%)
## 
## filter summary for frame 'Specimen_001_Sample_003_003.fcs'
##  green+: 9207 of 57249 events (16.08%)
## 
## filter summary for frame 'Specimen_001_Sample_004_004.fcs'
##  green+: 15370 of 57249 events (26.85%)
## 
## filter summary for frame 'Specimen_001_Sample_005_005.fcs'
##  green+: 6664 of 57249 events (11.64%)
## 
## filter summary for frame 'Specimen_001_Sample_006_006.fcs'
##  green+: 106 of 57249 events (0.19%)
## 
## filter summary for frame 'Specimen_001_Sample_007_007.fcs'
##  green+: 7186 of 57249 events (12.55%)
## 
## filter summary for frame 'Specimen_001_Sample_008_008.fcs'
##  green+: 5536 of 57249 events (9.67%)
## 
## filter summary for frame 'Specimen_001_Sample_009_009.fcs'
##  green+: 1117 of 57249 events (1.95%)
## 
## filter summary for frame 'Specimen_001_Sample_010_010.fcs'
##  green+: 13325 of 57249 events (23.28%)
## 
## filter summary for frame 'Specimen_001_Sample_011_011.fcs'
##  green+: 382 of 57249 events (0.67%)
## 
## filter summary for frame 'Specimen_001_Sample_012_012.fcs'
##  green+: 6302 of 57249 events (11.01%)
## 
## filter summary for frame 'Specimen_001_Sample_013_013.fcs'
##  green+: 9142 of 57249 events (15.97%)
## 
## filter summary for frame 'Specimen_001_Sample_014_014.fcs'
##  green+: 19647 of 57249 events (34.32%)
## 
## filter summary for frame 'Specimen_001_Sample_015_015.fcs'
##  green+: 7022 of 57249 events (12.27%)
## 
## filter summary for frame 'Specimen_001_Sample_016_016.fcs'
##  green+: 194 of 57249 events (0.34%)
## 
## filter summary for frame 'Specimen_001_Sample_017_017.fcs'
##  green+: 9707 of 57249 events (16.96%)
## 
## filter summary for frame 'Specimen_001_Sample_018_018.fcs'
##  green+: 20723 of 57249 events (36.20%)
## 
## filter summary for frame 'Specimen_001_Sample_019_019.fcs'
##  green+: 12320 of 57249 events (21.52%)
## 
## filter summary for frame 'Specimen_001_Sample_020_020.fcs'
##  green+: 8278 of 57249 events (14.46%)
## 
## filter summary for frame 'Specimen_001_Sample_021_021.fcs'
##  green+: 159 of 57249 events (0.28%)
## 
## filter summary for frame 'Specimen_001_Sample_022_022.fcs'
##  green+: 19576 of 57249 events (34.19%)
## 
## filter summary for frame 'Specimen_001_Sample_023_023.fcs'
##  green+: 12223 of 57249 events (21.35%)
## 
## filter summary for frame 'Specimen_001_Sample_024_024.fcs'
##  green+: 19027 of 57249 events (33.24%)
## 
## filter summary for frame 'Specimen_001_Sample_025_025.fcs'
##  green+: 18754 of 57249 events (32.76%)
## 
## filter summary for frame 'Specimen_001_Sample_026_026.fcs'
##  green+: 562 of 57249 events (0.98%)
## 
## filter summary for frame 'Specimen_001_Sample_027_027.fcs'
##  green+: 5194 of 57249 events (9.07%)
## 
## filter summary for frame 'Specimen_001_Sample_028_028.fcs'
##  green+: 7944 of 57249 events (13.88%)
## 
## filter summary for frame 'Specimen_001_Sample_029_029.fcs'
##  green+: 9030 of 57249 events (15.77%)
## 
## filter summary for frame 'Specimen_001_Sample_030_030.fcs'
##  green+: 22054 of 57249 events (38.52%)
## 
## filter summary for frame 'Specimen_001_Sample_031_031.fcs'
##  green+: 69 of 57249 events (0.12%)
## 
## filter summary for frame 'Specimen_001_Sample_032_032.fcs'
##  green+: 6328 of 57249 events (11.05%)
## 
## filter summary for frame 'Specimen_001_Sample_033_033.fcs'
##  green+: 414 of 57249 events (0.72%)
## 
## filter summary for frame 'Specimen_001_Sample_034_034.fcs'
##  green+: 9841 of 57249 events (17.19%)
## 
## filter summary for frame 'Specimen_001_Sample_035_035.fcs'
##  green+: 18004 of 57249 events (31.45%)
## 
## filter summary for frame 'Specimen_001_Sample_036_036.fcs'
##  green+: 30 of 57249 events (0.05%)
## 
## filter summary for frame 'Specimen_001_Sample_037_037.fcs'
##  green+: 16029 of 57249 events (28.00%)
## 
## filter summary for frame 'Specimen_001_Sample_038_038.fcs'
##  green+: 430 of 57249 events (0.75%)
## 
## filter summary for frame 'Specimen_001_Sample_039_039.fcs'
##  green+: 301 of 57249 events (0.53%)
## 
## filter summary for frame 'Specimen_001_Sample_040_040.fcs'
##  green+: 1550 of 57249 events (2.71%)
## 
## filter summary for frame 'Specimen_001_Sample_041_041.fcs'
##  green+: 239 of 57249 events (0.42%)
## 
## filter summary for frame 'Specimen_001_Sample_042_042.fcs'
##  green+: 6410 of 57249 events (11.20%)
## 
## filter summary for frame 'Specimen_001_Sample_043_043.fcs'
##  green+: 327 of 57249 events (0.57%)
## 
## filter summary for frame 'Specimen_001_Sample_044_044.fcs'
##  green+: 235 of 57249 events (0.41%)
## 
## filter summary for frame 'Specimen_001_Sample_045_045.fcs'
##  green+: 6141 of 57249 events (10.73%)
## 
## filter summary for frame 'Specimen_001_Sample_046_046.fcs'
##  green+: 109 of 57249 events (0.19%)
## 
## filter summary for frame 'Specimen_001_Sample_047_047.fcs'
##  green+: 6692 of 57249 events (11.69%)
## 
## filter summary for frame 'Specimen_001_Sample_048_048.fcs'
##  green+: 12634 of 57249 events (22.07%)
## 
## filter summary for frame 'Specimen_001_Sample_049_049.fcs'
##  green+: 11285 of 57249 events (19.71%)
## 
## filter summary for frame 'Specimen_001_Sample_050_050.fcs'
##  green+: 337 of 57249 events (0.59%)
## 
## filter summary for frame 'Specimen_001_Sample_051_051.fcs'
##  green+: 63 of 57249 events (0.11%)
## 
## filter summary for frame 'Specimen_001_Sample_052_052.fcs'
##  green+: 3705 of 57249 events (6.47%)
## 
## filter summary for frame 'Specimen_001_Sample_053_053.fcs'
##  green+: 749 of 57249 events (1.31%)
## 
## filter summary for frame 'Specimen_001_Sample_054_054.fcs'
##  green+: 2626 of 57249 events (4.59%)
## 
## filter summary for frame 'Specimen_001_Sample_055_055.fcs'
##  green+: 472 of 57249 events (0.82%)
## 
## filter summary for frame 'Specimen_001_Sample_056_056.fcs'
##  green+: 396 of 57249 events (0.69%)
## 
## filter summary for frame 'Specimen_001_Sample_057_057.fcs'
##  green+: 12624 of 57249 events (22.05%)
## 
## filter summary for frame 'Specimen_001_Sample_058_058.fcs'
##  green+: 427 of 57249 events (0.75%)
## 
## filter summary for frame 'Specimen_001_Sample_059_059.fcs'
##  green+: 4057 of 57249 events (7.09%)
## 
## filter summary for frame 'Specimen_001_Sample_060_060.fcs'
##  green+: 449 of 57249 events (0.78%)
## 
## filter summary for frame 'Specimen_001_Sample_061_061.fcs'
##  green+: 273 of 57249 events (0.48%)
## 
## filter summary for frame 'Specimen_001_Sample_062_062.fcs'
##  green+: 4919 of 57249 events (8.59%)
## 
## filter summary for frame 'Specimen_001_Sample_063_063.fcs'
##  green+: 371 of 57249 events (0.65%)
## 
## filter summary for frame 'Specimen_001_Sample_064_064.fcs'
##  green+: 156 of 57249 events (0.27%)
## 
## filter summary for frame 'Specimen_001_Sample_065_065.fcs'
##  green+: 1046 of 57249 events (1.83%)
## 
## filter summary for frame 'Specimen_001_Sample_066_066.fcs'
##  green+: 96 of 57249 events (0.17%)
## 
## filter summary for frame 'Specimen_001_Sample_067_067.fcs'
##  green+: 9452 of 57249 events (16.51%)
## 
## filter summary for frame 'Specimen_001_Sample_068_068.fcs'
##  green+: 468 of 57249 events (0.82%)
## 
## filter summary for frame 'Specimen_001_Sample_069_069.fcs'
##  green+: 170 of 57249 events (0.30%)
## 
## filter summary for frame 'Specimen_001_Sample_070_070.fcs'
##  green+: 442 of 57249 events (0.77%)
## 
## filter summary for frame 'Specimen_001_Sample_071_071.fcs'
##  green+: 183 of 57249 events (0.32%)
## 
## filter summary for frame 'Specimen_001_Sample_072_072.fcs'
##  green+: 6584 of 57249 events (11.50%)
## 
## filter summary for frame 'Specimen_001_Sample_073_073.fcs'
##  green+: 615 of 57249 events (1.07%)
## 
## filter summary for frame 'Specimen_001_Sample_074_074.fcs'
##  green+: 353 of 57249 events (0.62%)
## 
## filter summary for frame 'Specimen_001_Sample_075_075.fcs'
##  green+: 32 of 57249 events (0.06%)
## 
## filter summary for frame 'Specimen_001_Sample_076_076.fcs'
##  green+: 447 of 57249 events (0.78%)
## 
## filter summary for frame 'Specimen_001_Sample_077_077.fcs'
##  green+: 4667 of 57249 events (8.15%)
## 
## filter summary for frame 'Specimen_001_Sample_078_078.fcs'
##  green+: 167 of 57249 events (0.29%)
## 
## filter summary for frame 'Specimen_001_Sample_079_079.fcs'
##  green+: 458 of 57249 events (0.80%)
## 
## filter summary for frame 'Specimen_001_Sample_080_080.fcs'
##  green+: 111 of 57249 events (0.19%)
## 
## filter summary for frame 'Specimen_001_Sample_081_081.fcs'
##  green+: 58 of 57249 events (0.10%)
## 
## filter summary for frame 'Specimen_001_Sample_082_082.fcs'
##  green+: 3947 of 57249 events (6.89%)
## 
## filter summary for frame 'Specimen_001_Sample_083_083.fcs'
##  green+: 32 of 57249 events (0.06%)
## 
## filter summary for frame 'Specimen_001_Sample_084_084.fcs'
##  green+: 183 of 57249 events (0.32%)
## 
## filter summary for frame 'Specimen_001_Sample_085_085.fcs'
##  green+: 301 of 57249 events (0.53%)
## 
## filter summary for frame 'Specimen_001_Sample_086_086.fcs'
##  green+: 85 of 57249 events (0.15%)
## 
## filter summary for frame 'Specimen_001_Sample_087_087.fcs'
##  green+: 7934 of 57249 events (13.86%)
## 
## filter summary for frame 'Specimen_001_Sample_088_088.fcs'
##  green+: 76 of 57249 events (0.13%)
## 
## filter summary for frame 'Specimen_001_Sample_089_089.fcs'
##  green+: 161 of 57249 events (0.28%)
## 
## filter summary for frame 'Specimen_001_Sample_090_090.fcs'
##  green+: 195 of 57249 events (0.34%)
## 
## filter summary for frame 'Specimen_001_Sample_091_091.fcs'
##  green+: 62 of 57249 events (0.11%)
## 
## filter summary for frame 'Specimen_001_Sample_092_092.fcs'
##  green+: 16806 of 57249 events (29.36%)
## 
## filter summary for frame 'Specimen_001_Sample_093_093.fcs'
##  green+: 282 of 57249 events (0.49%)
## 
## filter summary for frame 'Specimen_001_Sample_094_094.fcs'
##  green+: 1377 of 57249 events (2.41%)
## 
## filter summary for frame 'Specimen_001_Sample_095_095.fcs'
##  green+: 3544 of 57249 events (6.19%)
## 
## filter summary for frame 'Specimen_001_Sample_096_096.fcs'
##  green+: 141 of 57249 events (0.25%)
## 
## filter summary for frame 'Specimen_001_Sample_097_097.fcs'
##  green+: 5387 of 57249 events (9.41%)
## 
## filter summary for frame 'Specimen_001_Sample_098_098.fcs'
##  green+: 139 of 57249 events (0.24%)
## 
## filter summary for frame 'Specimen_001_Sample_099_099.fcs'
##  green+: 163 of 57249 events (0.28%)
## 
## filter summary for frame 'Specimen_001_Sample_100_100.fcs'
##  green+: 67 of 57249 events (0.12%)

percentage = bind_rows(percent_red, percent_green) %>%
    group_by(name) %>% 
    mutate(total_gfp_mcherry = sum(true)) %>%
    mutate(fraction_fluor = round(true/total_gfp_mcherry, 3)) %>%
    mutate(percent_fluor = fraction_fluor*100) %>%
    mutate(label = paste(true, "events"))

ggplot() + 
    geom_point(data=events, aes(x=`530/30 Blue B-A`, y=`610/20 YG C-A`), shape=16, size=0.5, alpha=0.2, colour="black") +
    geom_point(data=events_green, aes(x=`530/30 Blue B-A`, y=`610/20 YG C-A`), shape=16, size=0.5, alpha=0.2, colour="green4") +
    geom_point(data=events_red, aes(x=`530/30 Blue B-A`, y=`610/20 YG C-A`), shape=16, size=0.5, alpha=0.2, colour="red3") +
    geom_text(data=percentage, aes(x=x, y=y, label=label, colour=colour), size=3, hjust=1) +
    geom_text(data=percentage, aes(x=2e5, y=4e5, label=paste0(count, " events total")), colour="grey27", size=3, hjust=1) +
    scale_colour_manual(values=c("green4", "red3")) +
    ggcyto::scale_y_flowjo_biexp(name="mCherry intensity", widthBasis=-500, pos=5, neg=0, breaks=c(0, 1e3,1e4,1e5), limits=c(-1e3, 5.5e5)) +
    ggcyto::scale_x_flowjo_biexp(name="GFP intensity", widthBasis=-500, pos=5, neg=0, breaks=c(0, 1e3,1e4,1e5), limits=c(-1e3, 3e5)) +
    theme_linedraw(12) +
    theme(panel.grid = element_blank(), 
          axis.title.x = element_text(colour="green4"), 
          axis.title.y = element_text(colour="red3"), 
          legend.position = "none") +
    facet_grid(Mouse~Timepoint) 

## Warning: Removed 3 rows containing missing values (geom_point).

ggsave("figures/exp17_GFP_mCherry_scatterplot.png", width=12, height=40, dpi=150)

## Warning: Removed 3 rows containing missing values (geom_point).

Make plots split on NT / GFPT

samples = metadata %>%
    dplyr::filter(Treatment == "NT") %>%
    pull(FlowSampleNumber)

percentage_samples = percentage %>%
    dplyr::filter(FlowSampleNumber %in% samples)

ggplot() + 
    geom_point(data=events[samples], aes(x=`530/30 Blue B-A`, y=`610/20 YG C-A`), shape=16, size=0.5, alpha=0.2, colour="black") +
    geom_point(data=events_green[samples], aes(x=`530/30 Blue B-A`, y=`610/20 YG C-A`), shape=16, size=0.5, alpha=0.2, colour="green4") +
    geom_point(data=events_red[samples], aes(x=`530/30 Blue B-A`, y=`610/20 YG C-A`), shape=16, size=0.5, alpha=0.2, colour="red3") +
    geom_text(data=percentage_samples, aes(x=x, y=y, label=label, colour=colour), size=3, hjust=1) +
    geom_text(data=percentage_samples, aes(x=2e5, y=4e5, label=paste0(count, " events total")), colour="grey27", size=3, hjust=1) +
    scale_colour_manual(values=c("green4", "red3")) +
    ggcyto::scale_y_flowjo_biexp(name="mCherry intensity", widthBasis=-500, pos=5, neg=0, breaks=c(0, 1e3,1e4,1e5), limits=c(-1e3, 5.5e5)) +
    ggcyto::scale_x_flowjo_biexp(name="GFP intensity", widthBasis=-500, pos=5, neg=0, breaks=c(0, 1e3,1e4,1e5), limits=c(-1e3, 3e5)) +
    theme_linedraw(12) +
    theme(panel.grid = element_blank(), 
          axis.title.x = element_text(colour="green4"), 
          axis.title.y = element_text(colour="red3"), 
          legend.position = "none") +
    facet_grid(Mouse_Treatment~Day) 

## Warning: Removed 1 rows containing missing values (geom_point).

ggsave("figures/exp17_GFP_mCherry_scatterplot_NT.png", width=12, height=20, dpi=150)

## Warning: Removed 1 rows containing missing values (geom_point).

samples = metadata %>%
    filter(Treatment == "GFPT") %>%
    pull(FlowSampleNumber)

percentage_samples = percentage %>%
    filter(FlowSampleNumber %in% samples)

ggplot() + 
    geom_point(data=events[samples], aes(x=`530/30 Blue B-A`, y=`610/20 YG C-A`), shape=16, size=0.5, alpha=0.2, colour="black") +
    geom_point(data=events_green[samples], aes(x=`530/30 Blue B-A`, y=`610/20 YG C-A`), shape=16, size=0.5, alpha=0.2, colour="green4") +
    geom_point(data=events_red[samples], aes(x=`530/30 Blue B-A`, y=`610/20 YG C-A`), shape=16, size=0.5, alpha=0.2, colour="red3") +
    geom_text(data=percentage_samples, aes(x=x, y=y, label=label, colour=colour), size=3, hjust=1) +
    geom_text(data=percentage_samples, aes(x=2e5, y=4e5, label=paste0(count, " events total")), colour="grey27", size=3, hjust=1) +
    scale_colour_manual(values=c("green4", "red3")) +
    ggcyto::scale_y_flowjo_biexp(name="mCherry intensity", widthBasis=-500, pos=5, neg=0, breaks=c(0, 1e3,1e4,1e5), limits=c(-1e3, 5.5e5)) +
    ggcyto::scale_x_flowjo_biexp(name="GFP intensity", widthBasis=-500, pos=5, neg=0, breaks=c(0, 1e3,1e4,1e5), limits=c(-1e3, 3e5)) +
    theme_linedraw(12) +
    theme(panel.grid = element_blank(), 
          axis.title.x = element_text(colour="green4"), 
          axis.title.y = element_text(colour="red3"), 
          legend.position = "none") +
    facet_grid(Mouse_Treatment~Day) 

## Warning: Removed 2 rows containing missing values (geom_point).

ggsave("figures/exp17_GFP_mCherry_scatterplot_GFPT.png", width=12, height=20, dpi=150)

## Warning: Removed 2 rows containing missing values (geom_point).

Make plots for representative mice

subset = c("Mouse 2", "Mouse 15")

samples = metadata %>% 
    filter(Mouse %in% subset) %>%
    pull(FlowSampleNumber)

percentage_samples = percentage %>% 
    filter(Mouse %in% subset)

ggplot() + 
    geom_point(data=events[samples], aes(x=`530/30 Blue B-A`, y=`610/20 YG C-A`), shape=16, size=0.5, alpha=0.2, colour="black") +
    geom_point(data=events_green[samples], aes(x=`530/30 Blue B-A`, y=`610/20 YG C-A`), shape=16, size=0.5, alpha=0.2, colour="green4") +
    geom_point(data=events_red[samples], aes(x=`530/30 Blue B-A`, y=`610/20 YG C-A`), shape=16, size=0.5, alpha=0.2, colour="red3") +
    geom_text(data=percentage_samples, aes(x=x, y=y, label=label, colour=colour), size=3, hjust=1) +
    geom_text(data=percentage_samples, aes(x=2e5, y=4e5, label=paste0(count, " events total")), colour="grey27", size=3, hjust=1) +
    scale_colour_manual(values=c("green4", "red3")) +
    ggcyto::scale_y_flowjo_biexp(name="mCherry intensity", widthBasis=-500, pos=5, neg=0, breaks=c(0, 1e3,1e4,1e5), limits=c(-1e3, 5.5e5)) +
    ggcyto::scale_x_flowjo_biexp(name="GFP intensity", widthBasis=-500, pos=5, neg=0, breaks=c(0, 1e3,1e4,1e5), limits=c(-1e3, 3e5)) +
    theme_linedraw(14) +
    theme(panel.grid = element_blank(), 
          panel.border = element_rect(size=1.25),
          axis.title.x = element_text(colour="green4"), 
          axis.title.y = element_text(colour="red3"), 
          legend.position = "none") +
    facet_grid(Treatment_Mouse~Day) 

ggsave("figures/exp17_GFP_mCherry_scatterplot_M2_M15.png", width=12, height=5, dpi=150)

Calculate and subtract background

#calculate max observed for green or red for later filtering
bg_green_high = percentage %>%
    dplyr::filter(population == "green+", Timepoint == -3) %>%
    pull(true) %>%
    max()

bg_red_high = percentage %>%
    dplyr::filter(population == "red+", Timepoint == -3) %>%
    pull(true) %>%
    max()

#set background for both green/red based on each mouse
percentage = percentage %>%
    group_by(Mouse, population) %>%
    mutate(bg = true[which(Timepoint == -3)]) %>%
    ungroup() %>%
    mutate(positive_events_minusbg = true - bg)

#set all negative values or 0 values to one
percentage = percentage %>%
    mutate(positive_events_minusbg_nozeros = as.double(positive_events_minusbg)) %>%
    mutate(positive_events_minusbg_nozeros = case_when(positive_events_minusbg_nozeros <= 0 ~ 1, 
                                               positive_events_minusbg_nozeros > 0 ~ positive_events_minusbg_nozeros))

Plot events over time for individual mice

ggplot(percentage) +
    annotate("rect", xmin=0.25, xmax=7.25, ymin=0, ymax=Inf, fill="honeydew2", colour=NA, alpha=0.75) +
    geom_line(aes(x=Timepoint, y=positive_events_minusbg_nozeros, colour=population, group=interaction(Mouse, population)), size=1) +
    geom_point(aes(x=Timepoint, y=positive_events_minusbg_nozeros, colour=population), size=2, alpha=1) +
    scale_y_log10(name="Positive events \n(background subtracted)\n", labels = trans_format("log10", math_format(10^.x))) + 
    scale_x_continuous(name="Day", breaks=seq(-4,15,2)) +
    scale_colour_manual(values=c("green4", "red3")) +
    theme_linedraw(12) +
    theme(panel.grid = element_blank(), panel.grid.major.y = element_blank(), panel.border=element_rect(size=1.5), legend.position = "top") +
    annotation_logticks(sides="l") +
    facet_wrap(~Mouse_Treatment)

## Warning: Transformation introduced infinite values in continuous y-axis

ggsave("figures/exp17_timecourse_green_red_byMouse_unfiltered.png",  height=8, width=12, dpi=150)

## Warning: Transformation introduced infinite values in continuous y-axis

#filter out timepoints where red AND green are both below the highest background observed for each multiplied by a factor
percentage_filtered = percentage %>%
    mutate(bg_high = case_when(population == "green+" ~ bg_green_high, population == "red+" ~ bg_red_high)) %>%  #background depending on fluor
    mutate(below_background = ifelse(true > bg_high * 3, "no", "yes")) %>%                                       #mark whether above or below background
    mutate(Mouse_Timepoint = paste(Mouse, Timepoint))

#make new table of mouse+timepoint to filter by 
filter_below = percentage_filtered %>%
    dplyr::select(Mouse, Timepoint, Mouse_Timepoint, population, bg, below_background) %>%
    dplyr::filter(below_background == "yes") %>% 
    dplyr::filter(Timepoint != -3) %>%
    pivot_wider(names_from=population, values_from=bg) %>%
    dplyr::filter(is.na(`green+`) == FALSE & is.na(`red+`) == FALSE)

`%notin%` <- Negate(`%in%`)

percentage_filtered = percentage_filtered %>%
    dplyr::filter(Mouse_Timepoint %notin% filter_below$Mouse_Timepoint)

ggplot(percentage_filtered) +
    annotate("rect", xmin=0.25, xmax=7.25, ymin=0, ymax=Inf, fill="honeydew2", colour=NA, alpha=0.75) +
    geom_point(aes(x=Timepoint, y=positive_events_minusbg_nozeros, colour=population), size=2, alpha=1) +
    geom_line(aes(x=Timepoint, y=positive_events_minusbg_nozeros, colour=population, group=interaction(Mouse, population)), size=1) +
    scale_y_log10(name="Positive events \n(background subtracted)\n", labels = trans_format("log10", math_format(10^.x))) + 
    scale_x_continuous(name="Day", breaks=seq(-4,15,2)) +
    scale_colour_manual(values=c("green4", "red3")) +
    theme_linedraw(12) +
    theme(panel.grid = element_blank(), panel.grid.major.y = element_blank(), panel.border=element_rect(size=1.5), legend.position = "top") +
    annotation_logticks(sides="l") +
    facet_wrap(~Mouse_Treatment)

## Warning: Transformation introduced infinite values in continuous y-axis

ggsave("figures/exp17_timecourse_green_red_byMouse.png", height=8, width=12, dpi=150)

## Warning: Transformation introduced infinite values in continuous y-axis

ggsave("figures/exp17_timecourse_green_red_byMouse.pdf", height=8, width=12)

## Warning: Transformation introduced infinite values in continuous y-axis

Facet events over time by treatment

percentage_filtered = percentage_filtered %>%
    mutate(Treatment_Full = gsub("NT", "Non-Targeting", Treatment)) %>%
    mutate(Treatment_Full = gsub("GFPT", "GFP-Targeting", Treatment_Full)) %>%
    mutate(Treatment_Full = factor(Treatment_Full, levels=c("Non-Targeting", "GFP-Targeting")))

set.seed(111)

#median
ggplot(percentage_filtered) +
    annotate("rect", xmin=0.75, xmax=7.75, ymin=0, ymax=Inf, fill="honeydew2", colour=NA, alpha=0.75) +
    geom_jitter(aes(x=Timepoint, y=positive_events_minusbg_nozeros, colour=population), position=position_jitterdodge(dodge.width=1.1), size=2, alpha=0.5) +
    stat_summary(aes(x=Timepoint, y=positive_events_minusbg_nozeros, group=population, colour=population), size=0.75, alpha=1, 
                 geom="line", position=position_dodge(width=0.5), fun = median) +
    stat_summary(aes(x=Timepoint, y=positive_events_minusbg_nozeros, group=factor(interaction(population, Timepoint)), colour=population), size=0.75, alpha=1, 
                 geom="pointrange", position=position_dodge(width=1), fun = median, fun.min = min, fun.max = max) +
    scale_y_log10(name="Positive events\n(background subtracted)", limits=c(1,40000)) + 
    scale_x_continuous(name="Day", breaks=seq(-4,15,2)) +
    scale_colour_manual(values=c("green4", "red3"), labels=c("GFP+", "mCherry+")) +
    theme_linedraw(14) +
    theme(panel.grid = element_blank(), panel.grid.major.y = element_blank(), panel.border=element_rect(size=1.5), legend.position = "top") +
    annotation_logticks(sides="l") +
    facet_wrap(~Treatment_Full, scales="free_y")

## Warning: Transformation introduced infinite values in continuous y-axis

ggsave("figures/exp17_timecourse_green_red_byTreatment_pointrange_median.png", width=12, height=6, dpi=150)

## Warning: Transformation introduced infinite values in continuous y-axis

ggsave("figures/exp17_timecourse_green_red_byTreatment_pointrange_median.pdf", width=12, height=6)

## Warning: Transformation introduced infinite values in continuous y-axis

Plot percent red / green at Day 14

dayfourteen = percentage_filtered %>%
    dplyr::filter(Timepoint == 14) 

#calculate percent positive green/red using background subtracted-positive events (no negatives)
dayfourteen = dayfourteen %>%
    mutate(positive_events_minusbg_nonegs = as.double(positive_events_minusbg)) %>%
    mutate(positive_events_minusbg_nonegs = case_when(positive_events_minusbg_nonegs <= 0 ~ 0, 
                                                      positive_events_minusbg_nonegs > 0 ~ positive_events_minusbg_nonegs)) %>%
    group_by(Mouse, Timepoint) %>%
    mutate(positive_events_minusbg_nonegs_gfp_mcherry_total = sum(positive_events_minusbg_nonegs)) %>%
    mutate(positive_events_minusbg_nonegs_percent_fluor = positive_events_minusbg_nonegs / positive_events_minusbg_nonegs_gfp_mcherry_total * 100) %>%
    ungroup()

#order mice by green fluor
green_ranked = dayfourteen %>%
    dplyr::filter(population=="green+") %>%
    arrange(Treatment, positive_events_minusbg_nonegs_percent_fluor) %>%
    pull(Mouse)

dayfourteen = dayfourteen %>% 
    mutate(Mouse = factor(Mouse, levels=rev(green_ranked))) %>%
    mutate(population = factor(population, levels=c("red+", "green+")))

#make df of labels to plot percentage red value on bar plot
labels = dayfourteen %>%
    dplyr::filter(population == "green+") %>%
    mutate(labels = paste0(round(positive_events_minusbg_nonegs_percent_fluor, 0), "%")) 

ggplot() +
     geom_bar(data=dayfourteen, aes(x=Mouse, y=positive_events_minusbg_nonegs_percent_fluor, fill=population), stat = "identity") +
     geom_text(data=labels, aes(x=Mouse, y=case_when(positive_events_minusbg_nonegs_percent_fluor > 10 ~ positive_events_minusbg_nonegs_percent_fluor - 4, 
                                                     positive_events_minusbg_nonegs_percent_fluor < 10 ~ positive_events_minusbg_nonegs_percent_fluor + 3),
                                label=labels), colour="white", size=3.5) +
     scale_fill_manual(values = c("red3", "green4"), labels=c("mCherry+", "GFP+")) +
     scale_y_continuous(name="Day 14  Percent GFP+ and mCherry+ events \n(background subtracted)", 
                        expand = c(0, 0), position="right", breaks=c(0,25,50,75,100), labels=c(0,25,50,75,100)) +
     scale_x_discrete(name = "", position = "top") +
     coord_flip() +
     theme_linedraw(14) +
     theme(legend.position = "bottom", legend.title=element_blank(), panel.grid=element_blank(),
           plot.margin=unit(c(5, 5, 5, 15),"mm"), 
           panel.border = element_rect(size=1.25)) +
     guides(fill = guide_legend(nrow = 1))

ggsave("figures/exp17_percent_green_red_barplot.pdf", height=6, width=7)

Calculate significance Day 14

dayfourteen_green = dayfourteen %>%
    dplyr::filter(population=="green+") %>%
    mutate(Treatment = factor(Treatment, levels=c("NT", "GFPT")))

nt = dayfourteen_green %>% 
    dplyr::filter(Treatment=="NT") %>%
    pull(positive_events_minusbg_nonegs_percent_fluor)

gfpt = dayfourteen_green %>% 
    dplyr::filter(Treatment=="GFPT") %>%
    pull(positive_events_minusbg_nonegs_percent_fluor)

(mean_nt = round(mean(nt), 0))

## [1] 81

(mean_gfpt = round(mean(gfpt), 0))

## [1] 6

(median_nt = round(median(nt), 0))

## [1] 99

(median_gfpt = round(median(gfpt), 0))

## [1] 1

#do mann whitney wilcoxon test for nonparametric distribution
(test = wilcox.test(gfpt, nt, alternative = "less"))

## Warning in wilcox.test.default(gfpt, nt, alternative = "less"): cannot compute
## exact p-value with ties

## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  gfpt and nt
## W = 2, p-value = 0.0002485
## alternative hypothesis: true location shift is less than 0

pvalue = formatC(test$p.value, format = "e", digits = 2) 

ggplot(dayfourteen_green, aes(x=Treatment, y=percent_fluor, fill=population)) +
    geom_bar(stat="summary", fun = median, colour="black", size=0.75, width=0.5) + 
    geom_jitter(width=0.2, height=0.2, shape=21, colour="black", fill="white", size=4, stroke=1) +
    scale_fill_manual(values=c("green4")) +
    scale_x_discrete(name="") +
    scale_y_continuous(name="Day 14  Percent GFP+ events \n(background subtracted)", expand=c(0,0), limits=c(0, 120), breaks=seq(0,100,20)) +
    annotate("text", x=1.5, y=114, label=paste("p =", pvalue), size=4) +
    annotate("text", x=1.025, y=median_nt+6, label=paste0("median ", median_nt,"%"), size=4, colour="green4") +
    annotate("text", x=2.025, y=median_gfpt+11, label=paste0("median ", median_gfpt,"%"), size=4, colour="green4") +
    geom_segment(x=1, xend=2, y=110, yend=110) +
    theme_linedraw(15) +
    theme(panel.border = element_rect(size=1.5), panel.grid = element_blank(), legend.position="none",
          axis.text.x = element_text(size=16), axis.ticks.x = element_blank())

ggsave("figures/exp17_Day14_green_barplot_pvalue_median.pdf", height=6, width=4)

Log session

sessionInfo()

## R version 4.0.3 (2020-10-10)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.2 LTS
## 
## Matrix products: default
## BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] forcats_0.5.0             stringr_1.4.0            
##  [3] dplyr_1.0.4               purrr_0.3.4              
##  [5] readr_1.3.1               tidyr_1.1.0              
##  [7] tibble_3.0.6              tidyverse_1.3.0          
##  [9] Phenoflow_1.1.2           foreach_1.5.0            
## [11] flowAI_1.19.1             flowFDA_0.99             
## [13] mclust_5.4.6              multcomp_1.4-13          
## [15] TH.data_1.0-10            MASS_7.3-53              
## [17] survival_3.2-7            mvtnorm_1.1-0            
## [19] flowFP_1.47.0             flowViz_1.53.0           
## [21] lattice_0.20-41           flowClean_1.27.0         
## [23] cowplot_1.0.0             scales_1.1.1             
## [25] ggcyto_1.17.0             flowWorkspace_4.1.1      
## [27] ncdfFlow_2.35.0           BH_1.72.0-3              
## [29] RcppArmadillo_0.9.900.1.0 ggplot2_3.3.2            
## [31] flowCore_2.1.0           
## 
## loaded via a namespace (and not attached):
##   [1] readxl_1.3.1         changepoint_2.2.2    backports_1.1.8     
##   [4] plyr_1.8.6           igraph_1.2.5         splines_4.0.3       
##   [7] digest_0.6.25        htmltools_0.4.0      fansi_0.4.1         
##  [10] magrittr_1.5         cluster_2.1.0        sfsmisc_1.1-7       
##  [13] recipes_0.1.12       Biostrings_2.57.1    modelr_0.1.8        
##  [16] gower_0.2.1          RcppParallel_5.0.1   matrixStats_0.56.0  
##  [19] sandwich_2.5-1       cytolib_2.1.2        jpeg_0.1-8.1        
##  [22] colorspace_1.4-1     rvest_0.3.5          blob_1.2.1          
##  [25] haven_2.3.1          xfun_0.14            crayon_1.3.4        
##  [28] jsonlite_1.6.1       hexbin_1.28.1        graph_1.67.1        
##  [31] zoo_1.8-8            iterators_1.0.12     ape_5.4             
##  [34] glue_1.4.2           gtable_0.3.0         ipred_0.9-9         
##  [37] zlibbioc_1.35.0      XVector_0.29.1       phyloseq_1.33.0     
##  [40] IDPmisc_1.1.20       Rgraphviz_2.33.0     Rhdf5lib_1.11.0     
##  [43] BiocGenerics_0.35.3  DBI_1.1.0            Rcpp_1.0.4.6        
##  [46] bit_1.1-15.2         stats4_4.0.3         lava_1.6.7          
##  [49] prodlim_2019.11.13   httr_1.4.1           RColorBrewer_1.1-2  
##  [52] ellipsis_0.3.1       farver_2.0.3         pkgconfig_2.0.3     
##  [55] XML_3.99-0.3         nnet_7.3-14          dbplyr_1.4.4        
##  [58] caret_6.0-86         labeling_0.3         tidyselect_1.1.0    
##  [61] rlang_0.4.10         reshape2_1.4.4       munsell_0.5.0       
##  [64] cellranger_1.1.0     tools_4.0.3          cli_2.0.2           
##  [67] generics_0.1.0       ade4_1.7-15          broom_0.5.6         
##  [70] evaluate_0.14        biomformat_1.17.0    yaml_2.2.1          
##  [73] ModelMetrics_1.2.2.2 knitr_1.28           fs_1.4.1            
##  [76] nlme_3.1-149         xml2_1.3.2           rstudioapi_0.11     
##  [79] compiler_4.0.3       png_0.1-7            reprex_0.3.0        
##  [82] stringi_1.4.6        Matrix_1.2-18        vegan_2.5-6         
##  [85] permute_0.9-5        multtest_2.45.0      vctrs_0.3.6         
##  [88] pillar_1.4.4         lifecycle_0.2.0      data.table_1.12.8   
##  [91] R6_2.4.1             latticeExtra_0.6-29  KernSmooth_2.23-17  
##  [94] gridExtra_2.3        RProtoBufLib_2.1.0   IRanges_2.23.6      
##  [97] codetools_0.2-16     boot_1.3-25          assertthat_0.2.1    
## [100] rhdf5_2.33.0         withr_2.2.0          S4Vectors_0.27.10   
## [103] mgcv_1.8-33          parallel_4.0.3       hms_0.5.3           
## [106] grid_4.0.3           rpart_4.1-15         timeDate_3043.102   
## [109] class_7.3-17         rmarkdown_2.2        pROC_1.16.2         
## [112] Biobase_2.49.0       lubridate_1.7.9.2